explanatory scope

local

Provides explanations for individual predictions

20 techniques

		Models	Data Types	Description
SHapley Additive exPlanations	Algorithmic	Model Agnostic	Any	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an...
Integrated Gradients	Algorithmic	Neural Network	Any	Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of...
DeepLIFT	Algorithmic	Neural Network	Any	DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between...
Layer-wise Relevance Propagation	Algorithmic	Neural Network	Any	Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to...
Contextual Decomposition	Algorithmic	Recurrent Neural Network	Text	Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from...
Taylor Decomposition	Algorithmic	Neural Network CNN	Any	Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and...
Local Interpretable Model-Agnostic Explanations	Algorithmic	Model Agnostic	Any	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex...
Individual Conditional Expectation Plots	Visualization	Model Agnostic	Any	ICE plots display the predicted output for individual instances as a function of a feature, with all other features held...
Saliency Maps	Algorithmic	Neural Network	Image	Saliency maps are visual explanations for image classification models that highlight which pixels in an image most...
Gradient-weighted Class Activation Mapping	Algorithmic	CNN	Image	Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making...
Occlusion Sensitivity	Algorithmic	Model Agnostic	Image	Occlusion sensitivity tests which parts of the input are important by occluding (masking or removing) them and seeing...
Classical Attention Analysis in Neural Networks	Algorithmic	Rnn CNN	Any	Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how...
Influence Functions	Algorithmic	Model Agnostic	Any	Influence functions quantify how much each training example influenced a model's predictions by computing the change in...
Contrastive Explanation Method	Algorithmic	Model Agnostic	Any	The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what...
ANCHOR	Algorithmic	Model Agnostic	Any	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of...
Counterfactual Fairness Assessment	Algorithmic	Model Agnostic	Any	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's...
Sensitivity Analysis for Fairness	Algorithmic	Model Agnostic	Any	Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or...
Neuron Activation Analysis	Algorithmic	Neural Network LLM +1	Text	Neuron activation analysis examines the firing patterns of individual neurons in neural networks by probing them with...
Concept Activation Vectors	Algorithmic	Neural Network Transformer +1	Any	Concept Activation Vectors (CAVs), also known as Testing with Concept Activation Vectors (TCAV), identify mathematical...
Attention Visualisation in Transformers	Algorithmic	Transformer	Text Image	Attention Visualisation in Transformers analyses the multi-head self-attention mechanisms that enable transformers to...

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